Assignment03

学生：张丽聪(12132234)

批改人：Juan-Li

得分： 24/50 （作业雷同且未注明）

import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import xarray as xr
from matplotlib import pyplot as plt

1. Niño 3.4 index

1.1 Compute monthly climatology for SST from Niño 3.4 region

da = xr.open_dataset("NOAA_NCDC_ERSST_v3b_SST.nc",engine="netcdf4")
weights = np.cos(np.deg2rad(da.lat))
sst_weighted = da.sst.sel(lat=slice(-5,5),lon=slice(210,240)).weighted(weights)
region_mean = sst_weighted.mean(dim=('lon','lat'))
monthly_sst = region_mean.groupby('time.month').mean()
monthly_sst

<xarray.DataArray 'sst' (month: 12)>
array([26.024014, 26.362394, 27.003092, 27.491852, 27.456303, 27.141228,
       26.58982 , 26.112291, 25.98521 , 25.974686, 25.978508, 25.953588],
      dtype=float32)
Coordinates:
  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12

1.1 Subtract climatology from SST time series to obtain anomalies

x = region_mean.size
anoma = np.zeros(x-2)
for i in range(0,x-2):
    k = i % 12
    anoma[i] = region_mean[i] - monthly_sst[k]
    
print(anoma)

[-3.30963135e-01 -4.54664230e-01 -2.80950546e-01 -2.05434799e-01
 -2.30035782e-01 -3.66180420e-01 -6.16817474e-02  1.39263153e-01
 -1.82481766e-01 -5.41425705e-01 -5.23983002e-01 -3.64625931e-01
 -3.14182281e-01 -2.28069305e-01 -2.70000458e-01 -7.27558136e-02
  1.59219742e-01  2.20666885e-01 -1.81058884e-01 -5.52108765e-01
 -1.00966263e+00 -9.02921677e-01 -6.00603104e-01 -4.50906754e-01
 -3.74057770e-01 -3.43709946e-01 -4.38940048e-01 -5.22491455e-01
 -6.29348755e-01 -4.49424744e-01 -2.49549866e-01 -2.83357620e-01
 -7.10056305e-01 -6.79294586e-01 -8.98977280e-01 -9.90926743e-01
 -7.22728729e-01 -3.17501068e-01  1.08222961e-01  3.90274048e-01
  8.86363983e-02  3.47993851e-01  8.60031128e-01  8.39574814e-01
  6.82573318e-01  8.26736450e-01  9.65261459e-01  1.19773102e+00
  8.20079803e-01  4.90779877e-01 -1.26127243e-01 -8.39115143e-01
 -1.07551765e+00 -1.11114883e+00 -6.98534012e-01 -1.13474846e+00
 -1.10546494e+00 -1.05469704e+00 -1.17579079e+00 -1.19849396e+00
 -7.82169342e-01 -3.96112442e-01 -2.02445984e-02  4.69264984e-02
  3.72009277e-01  6.20592117e-01  8.77420425e-01  1.22791100e+00
  1.32556534e+00  1.47460747e+00  1.67679214e+00  1.58392525e+00
  1.18647575e+00  8.05919647e-01  7.27197647e-01  4.18764114e-01
 -4.47118759e-01 -8.49971771e-02 -1.14250183e-02 -4.26727295e-01
 -5.45509338e-01 -4.80216980e-01 -5.26971817e-01 -6.34326935e-01
 -4.05538559e-01 -2.69256592e-01 -6.02115631e-01 -1.02444839e+00
 -3.98242950e-01  1.77974701e-02 -1.72922134e-01 -4.08275604e-01
 -8.08877945e-01 -7.55228043e-01 -6.49543762e-01 -7.67507553e-01
 -1.01075745e+00 -1.18670082e+00 -1.04150772e+00 -7.02096939e-01
 -6.85894012e-01 -2.74696350e-02  4.52142715e-01  2.59229660e-01
  5.73654175e-02  2.30892181e-01  5.01739502e-01  6.80013657e-01
  8.52725983e-01  9.68412399e-01  5.75172424e-01  5.08401871e-01
  6.01848602e-01  3.18761826e-01 -1.73568726e-03  2.45704651e-01
  5.52688599e-01  6.53747559e-01  5.33721924e-01  7.29692459e-01
  5.26790619e-01  1.70495987e-01  1.81064606e-02  2.34432220e-02
 -3.21191788e-01 -8.53246689e-01 -1.08705330e+00 -1.15822029e+00
 -9.06755447e-01 -1.08447266e+00 -1.05813026e+00 -1.21119308e+00
 -1.54037666e+00 -1.44580650e+00 -1.17505455e+00 -9.03314590e-01
 -7.57276535e-01 -9.16641235e-01 -8.74408722e-01 -9.15594101e-01
 -7.88028717e-01 -1.13399315e+00 -1.11263084e+00 -1.28592300e+00
 -8.63222122e-01 -4.85475540e-01 -9.25598145e-02  2.33209610e-01
  4.35466766e-01  6.56845093e-01  1.14994049e+00  1.51198196e+00
  1.49617195e+00  1.86521721e+00  2.17369080e+00  2.41566086e+00
  2.04671478e+00  1.22564125e+00  4.15952682e-01 -4.59506989e-01
 -9.85071182e-01 -9.98487473e-01 -1.40979385e+00 -1.40398598e+00
 -1.43551826e+00 -1.70537186e+00 -2.22385597e+00 -2.13230896e+00
 -1.97341156e+00 -1.55602837e+00 -1.17746544e+00 -8.21626663e-01
 -8.26705933e-01 -7.67297745e-01 -5.58120728e-01 -3.48073959e-01
 -4.64656830e-01 -7.81883240e-01 -1.06403923e+00 -8.48754883e-01
 -3.02171707e-01 -6.06157303e-01 -8.15513611e-01 -5.72696686e-01
 -8.02507401e-01 -1.28851318e+00 -1.09246254e+00 -1.10377312e+00
 -1.29801941e+00 -1.62760544e+00 -1.30681038e+00 -1.79863358e+00
 -1.88496017e+00 -1.14078712e+00 -6.24166489e-01 -6.89245224e-01
 -4.10305023e-01 -2.20394135e-02  2.56885529e-01  5.04529953e-01
  6.29997253e-01  9.35348511e-01  8.68396759e-01  7.75320053e-01
  9.83037949e-01  6.97431564e-01  4.47166443e-01 -2.34815598e-01
  1.58781052e-01  3.04380417e-01  1.99649811e-01 -6.58416748e-02
  3.94937515e-01  6.73301697e-01  5.98134995e-01  6.48046494e-01
  6.48948669e-01  3.75152588e-01 -4.02431488e-02 -5.61210632e-01
 -6.60833359e-01 -5.12590408e-01 -3.82158279e-01 -5.96029282e-01
 -5.56280136e-01 -1.66837692e-01 -1.40781403e-01  4.91142273e-03
 -2.34390259e-01 -1.72597885e-01  3.15475464e-01  3.02062988e-01
  7.25917816e-02 -1.04923248e-02 -2.50164032e-01  1.03847504e-01
  6.15066528e-01  4.35447693e-01  4.85567093e-01  5.51925659e-01
  5.96817017e-01  2.94038773e-01  7.90004730e-02 -5.22994995e-03
  9.21134949e-02  3.71648788e-01  9.26990509e-02 -2.12642670e-01
 -1.62235260e-01 -2.97018051e-01  1.83677673e-03  1.06241226e-01
 -3.52642059e-01 -6.17816925e-01 -4.60607529e-01 -3.21254730e-01
 -3.27144623e-01 -1.61096573e-01 -4.61927414e-01 -5.86065292e-01
 -1.13357544e-01 -1.06512070e-01 -2.61051178e-01 -1.08089447e-01
  2.29475021e-01  1.15242004e-01  1.17872238e-01  1.96640015e-01
  7.06979752e-01  8.37705612e-01  5.26569366e-01  9.94503021e-01
  1.79734612e+00  2.28138161e+00  2.42700195e+00  2.83947372e+00
  2.91114044e+00  2.34492111e+00  1.75108910e+00  1.32913780e+00
  1.31182098e+00  8.95385742e-01  2.70500183e-02 -1.74716949e-01
 -4.39016342e-01 -9.95325089e-01 -1.21386147e+00 -8.02986145e-01
 -3.19398880e-01  2.37405777e-01  7.32822418e-02 -2.20182419e-01
 -5.24604797e-01 -8.30909729e-01 -4.89503860e-01 -1.11373901e-01
 -2.38319397e-01 -5.72021484e-01 -1.08501625e+00 -1.24806786e+00
 -1.19357109e+00 -6.05335236e-01 -6.35246277e-01 -7.14372635e-01
 -6.42564774e-01 -6.83153152e-01 -6.44767761e-01 -6.63862228e-01
 -7.35221863e-01 -5.49530029e-01 -4.01174545e-01 -5.32310486e-01
 -6.23764038e-01 -4.49476242e-01 -1.47380829e-01  6.03370667e-02
 -5.95302582e-02  2.33345032e-02  3.03169250e-01  4.72887039e-01
  6.23832703e-01  8.93800735e-01  1.02175522e+00  1.22146225e+00
  1.30290985e+00  1.38529396e+00  1.32454491e+00  1.13621330e+00
  9.87726212e-01  1.06029320e+00  1.32797432e+00  1.69221306e+00
  1.77561378e+00  1.26540184e+00  1.24256897e+00  1.04600143e+00
  9.39210892e-01  1.84955597e-01  1.77923203e-01 -1.69374466e-01
 -9.96715546e-01 -1.70558929e+00 -1.82403374e+00 -1.27101135e+00
 -1.02985764e+00 -1.75619125e+00 -1.91534424e+00 -1.95112801e+00
 -1.81334496e+00 -1.22181511e+00 -1.02056885e+00 -7.60812759e-01
 -5.05804062e-01 -8.32557678e-02 -2.95034409e-01 -3.64562988e-01
 -2.32288361e-01 -3.42851639e-01 -3.97029877e-01 -1.03151321e-01
 -5.41038513e-02  1.71092987e-01 -2.44579315e-02  2.26760864e-01
  4.05338287e-01  7.17334747e-02  2.70549774e-01  3.50460052e-01
  3.16377640e-01  2.09562302e-01  7.55043030e-02  1.96365356e-01
  4.26448822e-01  3.24892044e-01  7.60478973e-02  3.65451813e-01
  5.78996658e-01  9.59886551e-01  1.11251450e+00  8.54352951e-01
  5.41435242e-01  8.24377060e-01  1.24568176e+00  1.63202095e+00
  1.85447693e+00  1.72328758e+00  1.41114616e+00  1.47615433e+00
  1.38861084e+00  8.21783066e-01  3.07975769e-01  1.07847214e-01
 -7.77606964e-02 -3.56052399e-01 -2.08093643e-01 -8.06407928e-02
  2.45565414e-01  5.71861267e-01  5.26227951e-01  1.00234413e+00
  1.13142204e+00  4.76942062e-01  4.32010651e-01  2.83992767e-01
  3.75091553e-01  2.86462784e-01  1.56719208e-01  7.67192841e-02
  1.55960083e-01  1.88282013e-01  1.48155212e-01  2.40964890e-01
  3.73693466e-01  4.32407379e-01  2.72422791e-01  2.86005020e-01
  2.83836365e-01  6.85518265e-01  1.21235275e+00  1.21360970e+00
  1.00916672e+00  7.54686356e-01  4.03032303e-01  1.82216644e-01
 -2.39391327e-01  2.19707489e-02 -8.00514221e-03 -6.58277512e-01
 -8.10800552e-01 -8.28147888e-01 -1.00267029e+00 -7.34460831e-01
 -6.39331818e-01 -6.38004303e-01 -4.84519958e-01 -2.89472580e-01
 -1.51567459e-01 -1.36272430e-01 -2.16087341e-01 -1.12840652e-01
 -3.65085602e-01 -3.01784515e-01 -3.25988770e-01 -6.78546906e-01
 -7.29331970e-01 -5.53371429e-01 -2.90473938e-01  2.87551880e-02
  7.96968460e-01  1.41324615e+00  1.88284302e+00  2.16668892e+00
  2.43552017e+00  2.78508377e+00  2.88759804e+00  2.86781883e+00
  2.80737877e+00  2.26365089e+00  1.69120789e+00  1.25765991e+00
  9.13053513e-01 -8.35552216e-02 -7.38901138e-01 -9.20829773e-01
 -1.18492889e+00 -1.28274727e+00 -1.08488274e+00 -1.44519424e+00
 -1.62586212e+00 -1.08290482e+00 -6.28887177e-01 -6.49038315e-01
 -7.72077560e-01 -8.52817535e-01 -8.57444763e-01 -9.65232849e-01
 -1.06307983e+00 -1.22986794e+00 -1.50417137e+00 -1.69520760e+00
 -1.78725243e+00 -1.38275337e+00 -6.30519867e-01 -3.87168884e-01
 -5.41465759e-01 -5.90938568e-01 -4.63226318e-01 -2.64402390e-01
 -3.23934555e-01 -5.30389786e-01 -6.52269363e-01 -6.04104996e-01
 -4.41150665e-01 -2.33337402e-01 -1.49421692e-01 -1.98699951e-01
 -3.01952362e-02 -4.86125946e-02  1.69277191e-02  4.79564667e-02
 -2.50669479e-01 -2.31401443e-01 -3.70431900e-01 -4.35684204e-01
 -3.32504272e-01 -1.46720886e-01  2.22520828e-01  1.80967331e-01
  5.76183319e-01  8.29935074e-01  7.32452393e-01  6.65700912e-01
  8.65821838e-01  1.05656624e+00  1.38643837e+00  1.49241447e+00
  1.16077805e+00  8.72938156e-01  5.21093369e-01 -8.93688202e-02
 -5.04625320e-01 -1.40525818e-01  5.62347412e-01  5.87287903e-01
  3.58514786e-01  6.22714996e-01  6.32682800e-01  6.62668228e-01
  4.75217819e-01  3.83752823e-01  3.09999466e-01  2.45405197e-01
  1.82857513e-01  3.37989807e-01  6.29901886e-01  7.74497986e-01
  7.67925262e-01  7.86268234e-01  6.77295685e-01  7.30808258e-01
  5.98949432e-01  2.80160904e-01  2.79836655e-01  2.64696121e-01
  5.09326935e-01  3.86175156e-01  3.64435196e-01  4.29010391e-01
  7.15179443e-02  2.35881805e-02 -8.67088318e-01 -1.12282562e+00
 -1.14631271e+00 -5.93595505e-01 -4.13314819e-01 -1.67369843e-01
  1.86334610e-01  2.44777679e-01  1.71716690e-01  4.34734344e-01
  6.07044220e-01  7.93394089e-01  1.22477341e+00  1.33336067e+00
  8.59451294e-01  8.75740051e-02 -2.89623260e-01 -4.35768127e-01
 -4.64048386e-01 -1.93256378e-01 -4.48507309e-01 -8.22219849e-01
 -9.36044693e-01 -9.79045868e-01 -1.22677040e+00 -1.30173683e+00
 -1.51760292e+00 -1.52563858e+00 -9.77664948e-01 -4.77426529e-01
 -3.43378067e-01 -2.53274918e-01  3.01227570e-02  1.88432693e-01
  1.54067993e-01  2.32234955e-01 -2.42431641e-01 -5.86479187e-01
 -6.26592636e-01 -4.15864944e-01 -4.24762726e-01 -1.29007339e-01
  3.35536957e-01  5.43600082e-01  5.77035904e-01  7.28227615e-01
  7.63870239e-01  9.35022354e-01  1.43855286e+00  1.80064392e+00
  1.61512184e+00  1.34798622e+00  9.90417480e-01  6.35974884e-01
  1.44687653e-01 -4.29273605e-01 -8.64080429e-01 -1.06239510e+00
 -1.33602142e+00 -1.38485718e+00 -1.45049477e+00 -1.36517525e+00
 -1.56078911e+00 -1.08126640e+00 -9.09395218e-01 -4.21377182e-01
 -8.37020874e-02  2.02125549e-01 -1.22554779e-01 -4.23446655e-01
 -6.64651871e-01 -8.22101593e-01 -1.00556564e+00 -1.04051399e+00
 -6.93565369e-01 -5.34931183e-01 -1.46629333e-01  2.02121735e-02
  4.74720001e-02  1.55788422e-01  3.21130753e-01  4.23690796e-01
  7.65533447e-01  4.70487595e-01  2.64822006e-01 -6.47014618e-01
 -9.54801559e-01 -6.95472717e-01 -2.74644852e-01  1.57421112e-01
 -8.35151672e-02 -3.44514847e-01 -2.91887283e-01 -3.76529694e-01
 -2.50135422e-01 -8.24470520e-02 -1.55830383e-01 -3.41175079e-01
 -4.39523697e-01 -6.64346695e-01 -3.01654816e-01  5.54828644e-02
  5.02494812e-01  3.12650681e-01  1.41061783e-01 -4.38919067e-02
  2.30691910e-01  5.74676514e-01  9.21855927e-01  7.70315170e-01
  6.29562378e-01  4.70785141e-01  5.29336929e-01  8.58333588e-01
  1.03217316e+00  1.18099022e+00  1.49545479e+00  1.79587364e+00
  2.12562943e+00  2.60789680e+00  2.95858192e+00  2.75204086e+00
  2.70796967e+00  2.30891037e+00  1.67467499e+00  1.20045280e+00
  4.08016205e-01 -1.21847153e-01 -6.37327194e-01 -6.85283661e-01
 -5.89282990e-01 -8.67475510e-01]

1.2 Visualize the computed Niño 3.4.

alist = ['JFM','FMA','MAM','AMJ','MJJ','JJA','JAS','OND','NDJ','DJF']
templist=[]
for i in range(0,68):
    for j in range(0,10):
        templist.append(alist[j])
templist.append('JFM')
templist.append('FMA')

def moving_average(x,w): # consecutive 3-month running mean SST anomalies exceed the threshold.
    return np.convolve(x, np.ones(w),'valid') / w
mean_anoma = moving_average(anoma,3) 

df = pd.DataFrame(mean_anoma,columns=['mean_3_anoma'])
df['mo'] = pd.DataFrame(templist)

def get_date_list(begin_date,end_date):
    date_list = [x.strftime('%Y-%m-%d') for x in list(pd.date_range(start=begin_date,end=end_date,freq='M'))]
    return date_list

date = get_date_list('1960-01-15','2016-12-15')
df['date'] = pd.DataFrame(date)

# Visualize the computed Niño 3.4.
df.plot(x="date",y="mean_3_anoma")
#plt.axhline(y=0.5,ls="-",c="red")
plt.axhline(y=0,ls=":",c="black")
#plt.axhline(y=-0.5,ls="-",c="blue")

<matplotlib.lines.Line2D at 0x1e3305412b0>

2. Earth’s energy budget

2.1 Make a 2D plot of the time-mean TOA longwave, shortwave, and solar radiation for all-sky conditions. Add up the three variables above and verify (visually) that they are equivalent to the TOA net flux.

d = xr.open_dataset('CERES_EBAF-TOA_200003-201701.nc',engine='netcdf4')

fig = plt.figure(figsize=(10,6), dpi=120)
grid = plt.GridSpec(5, 9)  

plt.subplot(grid[0:2, 0:3])
ax1 = d.toa_sw_all_mon.mean(dim='time').plot()
plt.title('TOA longwave flux for all-sky',fontsize=10)

plt.subplot(grid[0:2, 3:6])
ax2 = d.toa_lw_all_mon.mean(dim='time').plot()
plt.title('TOA shortwave flux for all-sky',fontsize=10)

plt.subplot(grid[0:2, 6:9])
ax3 = d.solar_mon.mean(dim='time').plot()
plt.title('TOA solar radiation for all-sky',fontsize=10)

plt.subplot(grid[2:5, 0:5])
ax4 = d.toa_net_all_mon.mean(dim='time').plot()
plt.title('TOA Net Flux All-Sky')

plt.subplot(grid[2:5, 4:10])
ax5 = (d.solar_mon.mean(dim='time') - d.toa_sw_all_mon.mean(dim='time') - d.toa_lw_all_mon.mean(dim='time') ).plot()
plt.title('Add up three variables')

plt.tight_layout()

2.2 Calculate and verify that the TOA incoming solar, outgoing longwave, and outgoing shortwave approximately match up with the cartoon above.

temp=[]
x = d.solar_mon.mean(dim='time') - d.toa_sw_all_mon.mean(dim='time') - d.toa_lw_all_mon.mean(dim='time')
y = d.toa_net_all_mon.mean(dim='time')
(x-y).sum()

<xarray.DataArray ()>
array(1.6990367, dtype=float32)

2.3 Calculate and plot the total amount of net radiation in each 1-degree latitude band

weights = np.cos(np.deg2rad(d.lat))
net_weighted = d.toa_net_all_mon.weighted(weights)
region_mean = net_weighted.sum(dim=['lon','time']).plot()
plt.xlabel('latitude/°',fontsize=15)
plt.ylabel('total amount of net radiation/ W',fontsize=15)

Text(0, 0.5, 'total amount of net radiation/ W')

2.4 Calculate and plot composites of time-mean outgoing shortwave and longwave radiation for low and high cloud area regions

fig = plt.figure(figsize=(10,6), dpi=120)
grid = plt.GridSpec(4, 4)  
plt.subplot(grid[0:2, 0:2])
d.toa_sw_all_mon.where((d.cldarea_total_daynight_mon<=25)).mean(dim='time').plot()
plt.title('sw-low cloud area regions')
plt.subplot(grid[0:2, 2:4])
d.toa_sw_all_mon.where((d.cldarea_total_daynight_mon>=75)).mean(dim='time').plot()
plt.title('sw-high cloud area regions')
plt.subplot(grid[2:4, 0:2])
d.toa_lw_all_mon.where((d.cldarea_total_daynight_mon<=25)).mean(dim='time').plot()
plt.title('lw-low cloud area regions')
plt.subplot(grid[2:4, 2:4])
d.toa_lw_all_mon.where((d.cldarea_total_daynight_mon>=75)).mean(dim='time').plot()
plt.title('lw-high cloud area regions')

plt.tight_layout()

2.5 Calculate the global mean values of shortwave and longwave radiation, composited in high and low cloud regions

fig = plt.figure(figsize=(18,6), dpi=600)
grid = plt.GridSpec(1, 2)  
plt.subplot(grid[0, 0])
d.toa_sw_all_mon.weighted(weights).mean(dim=['lon','lat']).plot(label='global')
d.toa_sw_all_mon.where((d.cldarea_total_daynight_mon>=75)).weighted(weights).mean(dim=['lon','lat']).plot(label='high cloud region')
d.toa_sw_all_mon.where((d.cldarea_total_daynight_mon<=25)).weighted(weights).mean(dim=['lon','lat']).plot(label='low cloud region')
plt.legend(loc='best')
plt.xlabel('time',fontsize=18)
plt.ylabel('toa_sw_all_mon',fontsize=18)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.subplot(grid[0, 1])
d.toa_lw_all_mon.weighted(weights).mean(dim=['lon','lat']).plot(label='global')
d.toa_lw_all_mon.where((d.cldarea_total_daynight_mon>=75)).weighted(weights).mean(dim=['lon','lat']).plot(label='high cloud region')
d.toa_lw_all_mon.where((d.cldarea_total_daynight_mon<=25)).weighted(weights).mean(dim=['lon','lat']).plot(label='low cloud region')
plt.legend(loc='best')
plt.xlabel('time',fontsize=18)
plt.ylabel('toa_lw_all_mon',fontsize=18)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.tight_layout()

3. Explore a netCDF dataset

3.1 Plot a time series of a certain variable with monthly seasonal cycle removed

T = xr.open_dataset('2016-2020-T.nc',engine="netcdf4")
T.t2m.mean(dim=('latitude', 'longitude'))

weight = np.cos(np.deg2rad(T.latitude))
T_weighted = T.t2m.weighted(weight)
region_mean = T_weighted.mean(dim=('longitude','latitude'))
a = region_mean.values
index = pd.date_range('2016-01-01', periods=1827, freq='D')
k = 0
t_day = np.zeros(1827)
for i in range(0,1827):
    t_day[i]=a[k:k+24].mean()
    k+=24
tempdf = pd.DataFrame(t_day,columns=['t2m'])
tempdf['date'] = index
tempdf['year'] = pd.DatetimeIndex(tempdf.date).year
tempdf['month'] = pd.DatetimeIndex(tempdf.date).month
t_month = tempdf.groupby(['year','month']).mean()


monthly_T = region_mean.groupby('time.month').mean()
sea = np.zeros(60)
for i in range(0,5):
    sea[12*i:12*i+12] = monthly_T

certain = t_month['t2m'] - sea
certain.plot()

<AxesSubplot:xlabel='year,month'>

3.2 Make at least 5 different plots using the dataset

T.t2m.mean(dim='time').plot()

<matplotlib.collections.QuadMesh at 0x1e3308c6ac0>

mu = 100 
sigma = 15 
x = region_mean.values
num_bins = 50

fig, ax = plt.subplots()

n, bins, patches = ax.hist(x, num_bins, density=1)

plt.title('Histogram of T: $\mu=100$, $\sigma=15$')
plt.xlabel('temperature')
plt.ylabel('probability dendity')
fig.tight_layout()
plt.show()

plt.boxplot(region_mean.values)

{'whiskers': [<matplotlib.lines.Line2D at 0x1e330277790>,
  <matplotlib.lines.Line2D at 0x1e3300840a0>],
 'caps': [<matplotlib.lines.Line2D at 0x1e330084430>,
  <matplotlib.lines.Line2D at 0x1e3300847c0>],
 'boxes': [<matplotlib.lines.Line2D at 0x1e3300aea60>],
 'medians': [<matplotlib.lines.Line2D at 0x1e330084b50>],
 'fliers': [<matplotlib.lines.Line2D at 0x1e330084ee0>],
 'means': []}

T_weighted.mean(dim=('longitude','latitude')).plot()

[<matplotlib.lines.Line2D at 0x1e330234190>]

monthly_T.plot()

[<matplotlib.lines.Line2D at 0x1e32cd82580>]